Cardiovascular diseases (CVD), such as e.g. atherosclerosis, hypertension and ischemia, remain the leading cause of death in most developed countries as they cause permanent damage to the heart and blood vessels that may lead to chronic heart failure, angina, or myocardial infarction (heart attack). For a patient showing symptoms of a cardiovascular disease, primary diagnosis and treatment are usually performed via interventional cardiology in a cardiac catheterization laboratory. Cardiac catheterization thereby means insertion of small tubes (catheters) through arteries and/or veins to the myocard. In order to visualize coronary arteries and cardiac chambers with real-time X-ray imaging, a contrast agent is injected through the catheter. The contrast agent has to be opaque to X-rays and provide good image contrast as it flows into the coronary artery system or into the cardiac chambers. This procedure produces an image referred to as an angiogram, which is standard for diagnosing cardiovascular disease.
In the last thirty years, minimally invasive X-ray guided interventional cardiology has grown considerably, fueled by demographic, technologic and economic factors. New catheter-based interventional tools (such as e.g. balloon catheters and stents) allow physicians to treat more conditions and more complicated patient cases. As these new minimally invasive, image-guided procedures have positive patient outcomes and are less costly than open-heart procedures, physicians are actively encouraged by governmental and private payers to use these procedures for treating patients.
Nowadays, X-ray based cardiac catheterization systems represent the current standard of care and provide imaging modalities for both diagnostic and therapeutic procedures in cardiology. They are applied for generating real-time images of obstructions to blood flow in the coronary arteries. When an obstruction is identified, real-time X-ray imaging is utilized to guide insertion of balloon-tipped catheters to the point of obstruction for treatment by angioplasty (which means by balloon expansion of the restricted flow area in the artery) and stent placement (that is, by expanding a supporting structure to keep the newly enlarged artery open). The goal of therapy for patients with coronary artery disease is to alleviate symptoms of angina and reduce the risk of death or myocardial infarction by employing techniques and devices for re-opening the coronary arteries.
A cardiac catheterization system as mentioned above virtually enables all minimally invasive procedures in a catheterization laboratory. Currently developed systems all have the same fundamental architecture and use a point X-ray source that projects an X-ray beam through the patient and onto a large-area detector, the latter being used for converting the generated fluoroscopic image to electrical signals for display on a monitor. Thereby, a shadowgram image of the patient is obtained.
Conventionally employed cardiac catheterization systems typically perform two distinct types of real-time X-ray imaging: diagnostic angiography and interventional imaging. Diagnostic angiography is performed with a high radiation exposure in order to produce high-quality images. This diagnostic (cine) mode produces images of injected contrast agent flowing through the coronary arteries to diagnose the initial condition of the coronary arteries, determine the intervention required, and re-evaluate the coronary arteries after the intervention. Interventional imaging is performed with a regulated radiation exposure that produces lower-quality images. This interventional (fluoro) mode thereby provides real-time imaging of a patient's anatomy to guide the intervention and is used when inserting devices into the anatomy. The interventional mode is used for approximately 90% of the procedure imaging time.
Today, virtually all currently available conventional X-ray based cardiac catheterization systems, such as for example those developed and marketed by Philips Healthcare, Siemens Healthcare, GE Healthcare and Toshiba Medical Systems, use the same fundamental imaging technology, which has not changed dramatically over the past 40 years. Incremental improvements to individual components have optimized system performance over decades close to the theoretical limits. However, current systems still exhibit high radiation exposure. The key problems thereby relate to imaging, radiation hazards and operational issues.
One of the most difficult imaging tasks in the cardiac catheterization lab is imaging patients at steep viewing angles. With conventional systems, a large-area detector close to the patient causes more scattered radiation reaching the detector than image radiation, which thus may severely degrade the obtained image quality. Therefore, physicians often use a high-radiation diagnostic (cine) mode during interventions to obtain better quality images.
Another serious problem consists in the fact that overlying anatomy may inhibit viewing and navigation. Conventional cardiac catheterization systems produce a shadowgram image that shows objects with no depth information. Discerning 3-D anatomy from these flat images is difficult. In addition, image clutter and shadowing of the heart by ribs or the spine often degrades image clarity.
A further problem conventional X-ray based cardiac catheterization systems are typically faced with is exposing both the patient and the interventionalist to a significant amount of radiation. Prolonged exposure can cause radiation skin burns on patients and increase the risk of cancer to the interventionalists and catheterization lab staff. Preventative measures for physicians include use of heavy and cumbersome wrap-around lead aprons, vests and thyroid shields.
As briefly mentioned above, percutaneous transluminal coronary angiography procedures are associated with a significant amount of X-ray dose. The main task of such procedures is to place catheters or cardiovascular stents at a given location in the interior of the myocard or in a cardiac blood vessel, respectively. This is usually done under guidance of intraoperative X-ray imaging in order to visualize the position of the catheter tip. Intraoperative application of fluoroscopic X-ray imaging is often necessary to provide answers for a large number of questions. This is especially true, for instance, if an interventionalist needs to visualize the morphology of cardiac blood vessels. Apart from being applied in various interventional disciplines to assist in the placement of cardiac pacemakers, surgical stents and guide wires, this imaging modality is also used in orthopedic traumatology to enable the position monitoring of medical implants, orthopedic protheses as well as surgical screws and nails. In cardiac X-ray images, on the other hand, specific high-density anatomical structures (such as e.g. the spine, specific vertebras, etc.) or foreign objects (such as e.g. pacemaker leads and surgical stitches, etc.) are most of the time visible in the X-ray image and may thus at least partly obstruct or jeopardize the visibility, detection and/or tracking of interventional tools, either because they create similar patterns or because they cast a shadow on the objects which shall be detected. Classical image subtraction techniques do not help in case of slowly moving interventional tools and would require new acquisitions of reference sequences every time the 2D view changes.
For diagnosis and prognosis of coronary disease as well as for performance of catheter-based coronary interventions, a quantitative description of the coronary arterial tree including its 3D geometry is advantageous (currently, only 2D images are available to most of the cardiologists). From the prior art, many computer-assisted techniques for reconstructing three-dimensional views of the coronary artery tree from bi-plane projection images or multiple single-plane projection images acquired from different gantry positions of a C-arm system are known. However, due to the problem of vessel overlap and perspective foreshortening, multiple projections are necessary to adequately reconstruct the coronary arterial tree with arteriography. The elimination or at least reduction of perspective foreshortening and overlap is a necessary prerequisite for an accurate quantitative coronary analysis (QCA), such as determination of intercoronary lengths in a 2D display.
The relevant literature describes that in CT imaging optimal view maps (OVMs), which are generated in an effort to reduce perspective foreshortening, may be applied to aid a user in obtaining a gantry position of the imaging device which results in an optimal view. The article “Optimizing Coronary Angiographic Views” (Int. Journal Cardiac Imaging, Supplement 1, vol. 1, pp. 53-54, 1995) by G. Finet and J. Lienard, for example, focuses only on minimization of vessel perspective foreshortening relative to a single arterial segment. From the relevant state of the art it is known that prior to the process of reconstructing a virtual 3D representation in a specific region of interest of a patient's body volume, which may e.g. include the patient's coronary artery tree or cardiac chambers anatomy, it may be provided to calculate an optimal view map associated to the image data of said body volume. From this OVM, an optimal viewing direction with least perspective foreshortening and minimum vessel overlap for displaying a virtual 3D representation of said region of interest can then e.g. be derived by means of a color coding for distinguishing between optimal and less optimal viewing angles. Aside form calculating optimal view maps based on pre-interventionally acquired image data, it is also known from the state of the art to automatically guide a user or system to this viewing angle for projection acquisition during an intervention procedure. Sometimes, the 3D information is used as a roadmap to steer the C-arm manually in the desired position for viewing a lesion while reducing contrast agent and radiation dose to which the patient is exposed.
In the articles “A Viewpoint Determination System for Stenosis Diagnosis and Quantification in Coronary Angiographic Acquisition” (IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 53-54, 1995) by Y. Sato, et al., and “3-D Coronary Angiography: Improving Visualization Strategy for Coronary Interventions” (in: Whats New In Cardiovascular Imaging, Kluwer Academic Publishers, pp. 61-67, 1998) by S. J. Chen and J. D. Carroll (hereinafter referred to as Chen and Carroll I), derivation of an optimal view strategy on the basis of minimization of both vessel overlap and perspective foreshortening is discussed. However, the technique devised by Sato requires a well-calibrated imaging system and manually specified correspondence in the 3D reconstruction process. Aside therefrom, the overlap measurement is limiting because it is performed based on the single stenotic segment with only immediate adjacent vessels. Sub-optimal solutions in determining optimal view are ineffective when the segment is more complex and more distal vessels were overlapped, both conditions of which are common in clinical conditions.
Conventional OVMs are typically utilized for an online reconstruction of a 3D arterial tree based on a pair of routine angiograms acquired from any two arbitrary viewing angles using single- or bi-plane imaging systems. A conventional process for reconstructing a virtual 3D representation of an object (such as e.g. a target structure or lesion) in a region of interest of a patient's cardiovascular system or cardiac anatomy which is optimized with respect to overlap and perspective foreshortening requires (a) an acquisition of two standard angiogram sequences by use of a single-plane imaging system, (b) an identification of 2D arterial trees and feature extractions, including bifurcation points, vessel diameters, vessel directional vertices, vessel centerlines, and construction of vessel hierarchies in the two images, (c) a determination of a transformation defining the spatial relationship of the acquired two views in terms of a rotation matrix and translation vector, and (d) a calculation of the 3D arterial (e.g., coronary) tree's arterial structures based thereon.
The approach discussed in the article “3-D Reconstruction of Coronary Arterial Tree to Optimize Angiographic Visualization” (IEEE Transactions on Medical Imaging, vol. 19, no. 4, April 2000) by S. J. Chen and J. D. Carroll (hereinafter referred to as Chen and Carroll II), on the other hand, requires considerable manual editing in order to retrieve the coronary tree. Chen and Carroll II thereby teaches the use two types of optimal view maps, a perspective foreshortening map and an overlap map, which two map types may be combined by the user to form a composite map, i.e. a “two-view” map.
Chen and Carroll II also teaches that an online 3D reconstruction technique, which is needed to reconstruct the entire coronary arterial trees based on two angiograms that have been acquired from two distinct projection directions without the need of a calibration object, and using a single-plane imaging system as well as a new optimization algorithm realized by minimizing the image point and vector angle errors in both views is subject to constraints which are derived from the intrinsic parameters of the single-plane imaging system.
Given the 3D character of the coronary artery tree, Chen and Carroll II expected that any projection would foreshorten a variety of segments. A reconstructed 3D coronary arterial tree may be rotated to any selected viewing angle yielding multiple computer-generated projections to determine for each patient which standard views are useful and which have no clinical value due to excessive overlap and perspective foreshortening. In this connection, Chen and Carroll II provide for computer-simulated projections for display with information of calculated percent perspective foreshortening and overlap on the screen such that a user may select any view by means of a keyboard input.
As an alternative to being reconstructed from pre-interventionally acquired CT or MR data sets which are to be registered with interventionally acquired fluoroscopic images, three-dimensional object representations can be obtained by means of straightforward 3D reconstruction or modeling of data sets from rotational C-arm based image acquisitions.
U.S. Pat. No. 7,340,033 B2 describes a method and a unit for automatically adjusting a collimator. In this connection, a region of interest inside the body is determined in an application-specific way from an analysis of first X-ray pictures, and the collimator is then adjusted thereon. The region of interest can, in particular, be chosen to be large enough for the irradiation field to cover all those positions of an organ of interest that occur as a result of heartbeat and/or respiration. Preferably, a data processing unit is designed to estimate the movement of the region of interest from an image analysis of subsequently acquired X-ray images during a current examination in order to be able to readjust the collimator if necessary. If the region of interest cannot be localized, the collimator is opened to a standard adjustment.
A method for automatically setting a collimator of an X-ray imaging system during image acquisition which includes receiving rapid scout images at an imaging station is disclosed in U.S. Pat. No. 6,055,295 A. The location of the body regions in one of said images is then automatically detected and used to generate settings for the collimator. The settings are used for automatically adjusting the collimator to substantially cover the non-body regions and substantially expose the body regions.
In U.S. Pat. No. 5,617,462 A, an automatic X-ray exposure control system and method for adjusting the X-ray dose/technique of X-ray diagnostic equipment to ensure sufficient doses/techniques for proper imaging while minimizing levels of radiation contacting the patient is described. A CCD video camera for analyzing the intensity of an acquired image is disposed adjacent to an X-ray receiver and opposite to an X-ray source. Said CCD video camera thereby provides two outputs, one of them being the absolute brightness as recorded by the camera. The obtained video signal is then analyzed by a windowing circuit or similar device to select an area of the image and restrict further processing of the image to that area. Circuits analyze the windowed area to detect the peak brightness and the average brightness within the windowed area. A microprocessor mathematically combines the readings to obtain a single value characteristic of the density of the piece of anatomy imaged by the X-ray equipment. The microprocessor then compares this value with one or more predetermined exposure control tables, determines the ideal dose/technique for imaging and adjusts the X-ray source to achieve ideal exposure. An automatic adjustment may then select predetermined techniques that may be used to minimize the X-ray radiation dose.